Content delivery method and system through in-vehicle nrtwork based on regional content popularity

ABSTRACT

The present disclosure discloses a content delivery method and system through an in-vehicle network based on regional content popularity, and in particular relates to the technical field of in-vehicle networks. For a content library corresponding to a target region and each target vehicle in the target region, each target vehicle responds to content objects in the content library. Based on each content object in the content library corresponding to the target region and a data eigenvalue of a preset data type corresponding to each content object, for each target vehicle in the target region that receives a data screening request, in response to the data screening request, content objects that match the data screening request are obtained and scheduled. A set of data screening requests responded to by each target vehicle is obtained by constructing a utility function.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application claims priority to Chinese patent application No.202111332459.8, filed on Nov. 11, 2021, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the technical field of in-vehiclenetworks, in particular to a content delivery method and system throughan in-vehicle network based on regional content popularity.

BACKGROUND

In recent years, with rapid development of intelligent transportationsystems, various in-vehicle applications, e.g., road safety, intelligenttransportation, in-vehicle entertainment, and driverless applications,have appeared in people’s lives. With the increase of the number ofvehicles, the communication load of an in-vehicle network increasessharply. Moreover, the contents requested by vehicles need to betransmitted from a content service center, which brings about a longrequest delay and results in lower user experience quality. In order toreduce the communication load of an in-vehicle network and reduce therequest delay of vehicles, caching technology has been introduced intothe in-vehicle network.

In the application of the caching technology to the existing in-vehiclenetworks, based on the cached content at each node in a network, a usermay directly obtain the required content from surrounding vehicle nodes,such that the communication link through which the requesting vehicleobtains the contents may be effectively shortened, but the redundancy ofthe contents in the network increases, and the requirements on thememory size and traffic size required by the network and a contentserver are higher.

The existing caching strategies mainly include Leave Copy Every (LCE),Caching with Probability, and Leave Copy Down (LCD). LCE means that thecontent will be cached by every network node through which the contentpasses. Caching with Probability means that a network node will cachethe content passing through the node with a certain fixed probability.LCD is to cache the content at the node next to a content source node.These caching strategies do not consider the content preferences ofusers around cache nodes, which may result in the case that a userrequest may not be responded to.

SUMMARY

The objective of the present disclosure is to provide a content deliverymethod and system through an in-vehicle network based on regionalcontent popularity to solve the problems in the prior art.

To achieve the above objective, the present disclosure provides thefollowing technical solutions:

-   A first aspect of the present disclosure provides-   A second aspect of the present disclosure provides a content    delivery system through an in-vehicle network based on regional    content popularity, including:-   A third aspect of the present disclosure provides a    computer-readable medium for storing software, the software    including instructions executable by one or more computers, and the    instructions, when executed by the one or more computers, performing    the steps of the content delivery method through an in-vehicle    network based on regional content popularity.

Using the above technical solutions, the content delivery method throughan in-vehicle network based on regional content popularity provided bythe present disclosure has the following technical effects compared withthe prior art:

In order to further improve the performance of the system, in a cachingprocess, the regional popularity is introduced, the content popularityof different regions is considered, and the cache ratio and thesimilarity of regional popularity of different content categories in atarget vehicle are expressed by cross-entropy. By maximizing thesimilarity, the caching strategy of the target vehicle is optimized, andthe nodes in the region cache the content with higher contentpopularity, so that the success rate that a user obtains the requestedcontent from the surrounding nodes is higher, and the cache utilizationof the target vehicle is also improved.

Moreover, in order to improve the content transmission quality of thetarget vehicle, the present disclosure constructs a utility function ofdata screening requests responded to by a target vehicle based on thedelay and the success rate, and obtains a set of data screening requestsresponded to by the target vehicle by maximizing the utility function,such that a compromise is achieved between the request success rate andthe request delay.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a schematic flowchart of a content delivery method throughan in-vehicle network according to an exemplary example of the presentdisclosure.

FIG. 2 shows a schematic diagram of a model of a system according to anexemplary example of the present disclosure.

FIG. 3 shows a schematic structural diagram of a neural networkaccording to an exemplary example of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

With reference to the schematic flowchart of the exemplary example ofthe present disclosure in FIG. 1 , the present disclosure provides acontent delivery method through an in-vehicle network based on regionalcontent popularity. Based on each content object in a content librarycorresponding to a target region and the data eigenvalue of a presetdata type corresponding to each content object, for each target vehiclein the target region that receives a data screening request, in responseto the data screening request, the following steps are performed toobtain and schedule content objects that match the data screeningrequest:

Step A: The data eigenvalue of the specified preset data type of eachcontent object in the content library corresponding to the target regionis extracted. The content category of each preset type corresponding toeach content object is obtained based on the data eigenvalue. For eachtarget vehicle, based on the content category and the data eigenvalue ofthe content object, each content category corresponding to all contentobjects is obtained. The cache ratio of all content objectscorresponding to each target vehicle under each content category isfurther obtained. Then, the content objects contained in each contentcategory are input into the target vehicle as the cached content of thetarget vehicle according to the cache ratio, that is, each targetvehicle obtains a cached content library containing each content object.Then, step B is performed.

Step B: For each target vehicle that receives the data screeningrequest, based on the interaction between the target vehicle and othertarget vehicles within a preset communication range, the delay that thetarget vehicle responds to the data screening request within the presetcommunication range and the success rate that the data screening requestis responded to are calculated. Based on the delay and the success ratethat the data screening request is responded to, each content objectcontained in the cached content library of the target vehicle, and thecache ratio of the cached content library of the target vehicle, targetcontent objects that match the data screening request are searched inthe cached content library. Then the utility that the data screeningrequest is responded to by the target vehicle is calculated andobtained, that is, the utility that each target vehicle responds to eachdata screening request is obtained. Then step C is performed.

Step C: For each target vehicle, based on the utility that thecorresponding data screening request is responded to by the targetvehicle, each data screening request responded to by the target vehicleis optimally screened, and a set of data screening requests responded toby the target vehicle is obtained.

Referring to FIG. 2 , using the process in step A, based on the dataeigenvalues of the specified preset data type of the content objects,each content object falls into the content category of each present typeby means of self-organizing clustering. The set of data screeningrequests in the target region is {1,...,j,...,J}, the set of targetvehicles is {1,...,b,...,B}, and for each content object c contained inthe cached content library of each target vehicle, the data eigenvaluesof Z data types corresponding to each content object c may be expressedas tr_(c) = [tr_(c,1), tr_(c,2), ..., tr_(c,z), ..., tr_(c,Z)], andtr_(c,z) represents the data eigenvalue of the zth data type of thecontent object c.

Content categorization is achieved by a neural network. With referenceto FIG. 3 , I content categories correspond to I neurons of asingle-layer neural network, and correspond to I central samples. Zconnection weights of the neurons correspond to the data eigenvalues ofthe central samples. The neural network includes Z input portscorresponding to Z eigenvalues of the content objects, and the neuralnetwork includes I output ports corresponding to I categories of thecontent objects. The output is y = [y_(1,) y₂, ..., y₁], and the inputand output may be expressed as

y_(i) = ∑_(Z = 1)^(Z)W_(i, z) ⋅ tr_(c, z), i=

1,2, ..., 1, where Wi = [W_(i,1), ..., W_(i,z), ..., W_(i,Z)], W_(i,Z)represents the zth connection weight of the ith neuron, the maximumeigenvalue y_(i), of v represents that the content c belongs to categoryi, W_(i) is updated with tr_(c), and the change of the connection weightW_(i) is η· (tr_(c) - W_(i)), where η is an adaptive constant.

Each target vehicle travels according to a preset route in a sub-targetregion. Assuming that the cache ratio of each target vehiclecorresponding to I content categories is r = [r₁, r₂, ..., r_(I)], theoptimization problem is constructed by cross-entropy as follows:

$\overset{min}{\lbrack {r_{1},r_{2},\ldots,r_{I}} \rbrack} - {\sum_{{}_{h \in H}}\frac{1}{w_{h}}} \cdot {\sum_{i = 1}^{I}p_{h,i}} \cdot log_{2}r_{i}$

where

$\overset{min}{\lbrack {r_{1},r_{2},\ldots,r_{1}} \rbrack}$

is the content category with the smallest cache ratio in the targetvehicle, w_(h) is the weight of distribution of a data screening requestin the hth sub-target region in H sub-target regions, p_(h,i) is theregional popularity corresponding to the ith content category containedin the target vehicle in the hth sub-target region, r_(i) is the cacheratio of the ith content category in the target vehicle, i ranges from 1to I, and the regional popularity is one data type of the specifiedpreset data types.

The corresponding constraints of the optimization problem are:

s.t.∑_(i = 1)^(l)r_(i) = 10 ≤ r_(i) ≤ 1

According to the constraints, the optimization problem is solved, andthe cache ratio of each content category in each target vehicle isobtained.

By the process in step B. assuming that a data screening request j is toobtain a target content object l from a target vehicle b, and theaverage time that the target vehicle b transmits the content to the datascreening request j is

$\frac{V}{R^{ave}},$

when T_(b,j) > t_(j), the delay is

$T_{b,j} - t_{j} + \frac{V}{R^{ave}},$

and the service time of the vehicle is

$\lbrack {T_{b,j},T_{b,j} + \frac{V}{R^{ave}}} \rbrack;$

when

$T_{b\, j} < t_{j} \cap T_{b,j} + \frac{V}{R^{ave}} \geq t_{j} + \frac{V}{R^{ave}},$

i.e.,

$t_{j} + \frac{V}{R^{ave}} - \frac{2R}{v} \leq T_{b,j} < t_{j},$

the delay is

$\frac{V}{R^{ave}},$

and the service time of the vehicle is

$\lbrack {t_{j},t_{j} + \frac{V}{R^{ave}}} \rbrack;$

when

$T_{b,j} < t_{j} \cap T_{b,j} + \frac{V}{R^{ave}} < t_{j} + \frac{V}{R^{ave}},$

i.e.,

$T_{b,j} < t_{j} + \frac{V}{R^{ave}} - \frac{2R}{v},$

the delay is

$T_{b,j} - 2t_{j} - \frac{R}{v} + T^{inter} + \frac{V}{R^{ave}},$

and the service time of the vehicle is

$\lbrack {T_{b,j} - 2t_{j} - \frac{R}{v} + T^{inter},T_{b,j} - 2t_{j} - \frac{R}{v} + T^{inter} + \frac{V}{R^{ave}}} \rbrack,$

where T^(inter) represents the departure interval between the targetvehicles: and then the delay D_(b,j) that the data screening request /obtains the content I from the target vehicle b is:

$D_{b,j} = \{ \begin{array}{l}\begin{array}{ll}{\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} T_{b,j} - t_{j} + \frac{V}{R^{ave}},} & {\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} T_{b,j} > t_{j}}\end{array} \\\begin{array}{ll}{\frac{V}{R^{ave}},} & {\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} t_{j} + \frac{V}{R^{ave}} - \frac{2R}{v} \leq T_{b,j} < t_{j}}\end{array} \\{T_{b,j} - 2t_{j} - \frac{R}{v} + T^{inter} + \frac{V}{R^{ave}},T_{b,j} < t_{j} + \frac{V}{R^{ave}} - \frac{2R}{v}}\end{array} )$

The time t_(b) that the target vehicle b arrives a certain sub-targetregion po obeys normal distribution, and the probability densityfunction f(t_(b)) of t_(b) is:

$f( t_{b} ) = \frac{1}{\sqrt{2\pi\delta^{2}{}_{po}}}\exp( {- \frac{( {t_{b} - \mu_{po}} )^{z}}{2\delta^{2}{}_{po}}} )$

where u_(po) is the mathematical expectation of t_(b), and δ² _(po) isthe variance of t_(b). The probability density function reflects theactual situation of the delay and the success rate that the datascreening request is responded to in the sub-target region.

The distance of a preset communication range of the target vehicle is R,the rate at which the target vehicle transmits the content is R^(ave),and the average travel speed of the target vehicle is v. Assuming thatthe times when the target vehicle b arrives and leaves the presetcommunication range are T_(b),_(j) and

$T_{b,j} + \frac{2R}{v}$

respectively, the conditions for the target vehicle b to enter theuser’s communication range within a tolerance time range of a user jare: when t_(j) < T_(b),_(j,) the condition for the target vehicle b tosuccessfully transmit the content object l is T_(b,j) < t_(j) + T and

$\frac{2R}{v} \geq \frac{V}{R^{ave}};$

when T_(b,j) <

$t_{j} < T_{b,j} + \frac{2R}{v},$

the condition for the target vehicle b to successfully transmit thecontent object l is

$T_{b,j} + \frac{2R}{v} - t_{j} \geq \frac{v}{R^{ave}};$

and when

$t_{j} > T_{b,j} + \frac{2R}{v},$

the target vehicle b cannot successfully transmit the content object l.Then from formula (2), the probability F_(b,j) that the target vehicle bsuccessfully transmits the content is:

$F_{b,j} = \{ \begin{array}{l}{\quad{\int_{t_{f}}^{t_{j + T}}{f(t)dt \cdot q\{ {\frac{2R}{v} \geq \frac{v}{R^{ave}}} \},\quad t_{j} < T_{b,j}}}} \\{\int_{t_{j - \frac{2R}{v}}}^{t_{j}}{f(t)dt \cdot {\int_{t_{f + \frac{V}{R^{ave}} - \frac{2R}{v}}}^{+ \infty}{f(t)dt,T_{b,j} < t_{j} < T_{b,j} + \frac{2R}{v}}}}} \\{\quad\mspace{6mu}\quad\quad\mspace{6mu}\quad\quad 0,\quad\quad\quad t_{j} > T_{b,j} + \frac{2R}{v}}\end{array} )$

The utility U_(b),_(j) that the data screening request J is responded toby the target vehicle b is obtained:

$U{}_{b,j} = \alpha\mspace{6mu} \cdot \mspace{6mu} F_{b,j} \cdot \mspace{6mu} g_{b,c}{}_{j}\mspace{6mu} + ( {1\mspace{6mu} + \mspace{6mu}\alpha} )\frac{\frac{1}{D_{b,j}}}{\sum{{}_{b = 1}^{B}\frac{1}{D_{b,j}}}}$

where a is the proportional coefficient and 0≤ a ≤1, B is the totalnumber of target vehicles contained in the target region, and g_(b,cj)is the probability that the target vehicle b obtains the target contentobject C_(j) corresponding to the data screening request j.

$g_{b,c_{j}} = \{ \begin{matrix}{\frac{c_{j} \cdot r_{b,k_{\tau}}}{N_{k_{\tau}}},c \cdot r_{b,k_{\tau}} < N_{k_{\tau}}} \\{1,N_{k_{\tau}} \geq c \cdot r_{b,k_{\tau}}}\end{matrix} )$

where k_(τ) is the content category of the target content object τ,τ_(b) is the cache ratio corresponding to each content categorycontained in the target vehicle b, 1 ≤ k_(τ) ≤ I, and N_(kτ) is thenumber of content objects contained in the content category k_(τ).

By the process in step C, the optimization problem is solved, a set ofdata screening requests responded to by the target vehicle b isobtained, b ∈ [1, B], and the set of data screening requests is storedin a content library as historical data for a new round of datascreening requests, such that the amount of calculation of a system isreduced and the success rate of requests is further improved.

What is claimed is:
 1. A content delivery method through an in-vehiclenetwork based on regional content popularity, wherein based on a contentlibrary corresponding to a target region and each target vehicle in thetarget region, each target vehicle responds to the content objects inthe content library; based on each content object in the content librarycorresponding to the target region and a data eigenvalue of a presetdata type corresponding to each content object, for each target vehiclein the target region that receives a data screening request, in responseto the data screening request, the following steps are performed toobtain and schedule content objects that match the data screeningrequest: step A: extracting the data eigenvalue of the specified presetdata type of each content object in the content library corresponding tothe target region: obtaining the content category of each preset typecorresponding to each content object based on the data eigenvalue; foreach target vehicle, based on the content category and the dataeigenvalues of the content objects, obtaining each content categorycorresponding to all content objects; further obtaining the cache ratioof all content objects corresponding to each target vehicle under eachcontent category; then, inputting the content objects contained in eachcontent category into the target vehicle as the cached content of thetarget vehicle according to the cache ratio, that is, each targetvehicle obtaining a cached content library containing each contentobject; and then performing step B; step B: for each target vehicle thatreceives the data screening request, based on the interaction betweenthe target vehicle and other target vehicles within a presetcommunication range, calculating the delay that the target vehicleresponds to the data screening request within the preset communicationrange and the success rate that the data screening request is respondedto; based on the delay and the success rate that the data screeningrequest is responded to, each content object contained in the cachedcontent library of the target vehicle, and the cache ratio of the cachedcontent library of the target vehicle, searching target content objectsthat match the data screening request in the cached content library;then calculating and obtaining the utility that the data screeningrequest is responded to by the target vehicle, that is, obtaining theutility that each target vehicle responds to each data screeningrequest; and then performing step C; step C: for each target vehicle,based on the utility that the corresponding data screening request isresponded to by the target vehicle, optimally screening each datascreening request responded to by the target vehicle, and obtaining aset of data screening requests responded to by the target vehicle. 2.The content delivery method through an in-vehicle network based onregional content popularity according to claim 1, wherein in the step A,based on the data eigenvalues of the specified preset data types of thecontent objects, each content object falls into the content category ofeach present type by means of self-organizing clustering.
 3. The contentdelivery method through an in-vehicle network based on regional contentpopularity according to claim 1, wherein the target region furthercomprises each sub-target region, and the step A specifically comprises:for each content object contained in the cached content library of eachtarget vehicle, based on the data eigenvalue of the preset data typecorresponding to each content object, assuming that the cache ratio ofeach target vehicle corresponding to I content categories is r= [r₁,r₂,...,r₁], the optimization problem is constructed by cross-entropy asfollows: $\begin{matrix}{min} \\\lbrack {r_{1},r_{2},\mspace{6mu}\ldots,r_{1}} \rbrack\end{matrix} - {\sum\limits_{h \in H}{\frac{1}{w_{h}} \cdot}}{\sum\limits_{i = 1}^{1}{p_{h,i} \cdot log_{2}r_{i}}}$where $\begin{array}{l}{min} \\{\lbrack r_{1},r_{2},...,r_{I}\rbrack}\end{array}$ is the content category with the smallest cache ratio inthe target vehicle, w_(h) is the weight of distribution of a datascreening request in the hth sub-target region in H sub-target regions,p_(h),_(i) is the regional popularity corresponding to the ith contentcategory contained in the target vehicle in the hth sub-target region,r_(i) is the cache ratio of the ith content category in the targetvehicle, and i ranges from 1 to I; the corresponding constraints of theoptimization problem are:$s.t.{\sum\limits_{i = 1}^{I}{r_{i} = 1\mspace{6mu} 0 \leq r_{i} \leq 1}}$according to the constraints, the optimization problem is solved, andthe cache ratio of each content category in each target vehicle isobtained.
 4. The content delivery method through an in-vehicle networkbased on regional content popularity according to claim 1, wherein thestep B comprises the following steps: step B1: for each target vehiclethat receives the data screening request, according to the followingformula: $D_{b,j} = \{ \begin{array}{l}{\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} T_{b,j} - t_{j} + \frac{V}{R^{ave}},\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} T_{b,j} > t_{j}} \\{\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\frac{V}{R^{ave}},\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} t_{j} + \frac{V}{R^{ave}} - \frac{2R}{v} \leq T_{b,j} < t_{j}} \\{T_{b,j} - 2t_{j} - \frac{R}{v} + T^{inter} + \frac{V}{R^{ave}},\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} T_{b,j} < t_{j} + \frac{V}{R^{ave}} - \frac{2R}{v}}\end{array} )$ the delay D_(b,j)} that the data screening requestj is responded to by the target vehicle b is obtained, where T_(b,j) isthe time that the target vehicle b obtains the data screening request jwithin the preset communication range, V is the average size of thespace occupied by the cached content library, v is the average drivingspeed of the target vehicle, R is the distance of the presetcommunication range of the target vehicle, R^(ave) is the rate at whichthe target vehicle transmits the target content object, t_(j) is thetolerance time that the data screening request j is responded to by thetarget vehicle, and T^(inter) is the time interval between the targetvehicles; step B2: according to the following formula:$F_{b,j} = \{ \begin{array}{l}{\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}{\int_{t_{j}}^{t_{j} + T}{f(t)dt \cdot q\{ {\frac{2R}{v} \geq \frac{V}{R^{ave}}} \},\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} t_{j} < T_{b,j}}}} \\{\int_{t_{j} - \frac{2R}{v}}^{t_{j}}{f(t)dt \cdot {\int_{t_{j + \frac{V}{R^{ave}} - \frac{2R}{v}}}^{+ \infty}{f(t)dt,\mspace{6mu} T_{b,j} < t_{j} < T_{b,j} + \frac{2R}{v}}}}} \\{\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} 0,\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} t_{j} > T_{b,j} + \frac{2R}{v}}\end{array} )$ the success rate F_(b,j) that the data screeningrequest j obtains the target content object from the cached contentlibrary corresponding to the target vehicle b is obtained, where T isthe tolerance time that the data screening request is responded to, q isthe probability,$q\{ {\frac{2R}{v} \geq \frac{V}{R^{ave}}} \}$ represents 0or 1, when$\frac{2R}{v} \geq \frac{V}{R^{ave}},q\{ {\frac{2R}{v} \geq \frac{V}{R^{ave}}} \} = 1$, otherwise$q\{ \frac{2R}{v} ) \leq ( \frac{V}{R^{ave}} \} = 0;$step B3: according to the following formula:$U_{b,j} = \alpha \cdot F_{b,j} \cdot g_{b,cj} + ( {1 + \alpha} )\frac{\frac{1}{D_{b,j}}}{\sum_{b = 1}^{B}\frac{1}{D_{b}{}_{,}j}}$the utility U_(b,j) that the data screening request j is responded to bythe target vehicle b is obtained, where a is the proportionalcoefficient, B is the total number of the target vehicles contained inthe target region, and g_(b,cj) is the probability that the targetvehicle b obtains the target content object c_(j) corresponding to thedata screening request j, $g_{b,c_{j}} = \{ \begin{array}{l}{\frac{c_{j} \cdot r_{b,k_{\tau}}}{N_{k_{\tau}}},\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} c \cdot r_{b,k_{\tau}}\mspace{6mu} < \mspace{6mu} N_{k_{\tau}}} \\{\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} 1,\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} N_{k_{\tau}} \geq c \cdot r_{b,k_{\tau}}}\end{array} )$ where k_(r) is the content category of the targetcontent object r, r_(b) is the cache ratio corresponding to each contentcategory contained in the target vehicle b, 1 ≤ k_(τ) ≤ I, and N_(kτ) isthe number of content objects contained in the content category k_(τ).5. The content delivery method through an in-vehicle network based onregional content popularity according to claim 4, wherein in the step C.the optimally screening each data screening request responded to by thetarget vehicle, and constructing the optimization problem is as follows:$\underset{set_{b},b\mspace{6mu} \in {\{{1,2,\mspace{6mu}\ldots,\mspace{6mu} B}\}}}{max}{\sum\limits_{b = 1}^{B}{\sum\limits_{j \in set_{b}}U_{b,j}}}$where set_(b) is a set of data screening requests responded to by thetarget vehicle b, and the corresponding constraints of the optimizationproblem are: $s.t.\begin{matrix}{set_{b1} \cap set_{b2} = \varnothing,b1 \neq b2,\mspace{6mu}\mspace{6mu}\mspace{6mu} b1,b2 \in \lbrack {1,B} \rbrack} \\{\mspace{6mu}\mspace{6mu}\mspace{6mu} set_{1} \cup set_{2} \cup \ldots \cup set_{j} = \{ {1,2,\ldots,j,\ldots,J} \}}\end{matrix}$ where J is the total number of the data screeningrequests.
 6. A content delivery system through an in-vehicle networkbased on regional content popularity, comprising: one or moreprocessors; and a memory, configured to store operable instructions, theinstructions, when executed by the one or more processors, causing theone or more processors to perform operations, and the operationscomprising executing the content delivery method through an in-vehiclenetwork based on regional content popularity according to claim
 1. 7. Acomputer-readable medium for storing software, wherein the softwarecomprises instructions executable by one or more computers, and theinstructions, when executed by the one or more computers, perform thecontent delivery method through an in-vehicle network based on regionalcontent popularity according to claim 1.